Channel Using Cognitive Radio Approach Computer Science Essay

Published Date: 02 Nov 2017

V.R.Vijaykumar, Member IEEE, K.Ashok, and K.Nithya

Department of Electronics and Communication Engineering, Anna University of Technology, Coimbatore, TamilNadu, India

[email protected]

Abstract— In this paper, image is transmitted over MIMO channel using cognitive radio approach is proposed. Compressive sampling is used for spectrum sensing with minimum system power consumption. The dynamic spectrum sensing in Cognitive Radio (CR) is done by the modulated wideband converter (MWC) for detecting the occupancy of the licensed user. The parameter of the system can be changed according to channel estimation through compressive sampling. The OFDM modulation is used to modulate the compressed image. The system power consumption is optimized in the multiple input multiple output (MIMO) communication system according to the channel estimation. The optimal and sub optimal algorithms are developed under the rate constraint to minimize the power consumption of the system. The power allocation algorithm of conventional method i.e. Waterfilling algorithm is compared with the quasi optimal algorithm, sub optimal 1 – Branch adaptation Algorithm and sub optimal 2 – Branch Minimization algorithm. The simulation results show that significant power savings up to 80% is achieved when compared with the conventional methods.

Keywords- Cognitive radio, Compressive sampling, MIMO, OFDM modulation, Power Minimization

Introduction (Heading 1)

Multimedia data transmission over wireless channels has become more popular today due to the tremendous development in wireless cellular systems. Implementing multiple input and multiple output (MIMO) system provides easy transmission of image and video over wireless channels. The spatial dimension in MIMO channels provides an extra dimension to separate users, allowing more aggressive reuse of time and frequency resources, thereby increasing the network capacity [1].

Cognitive radio is an intelligent wireless communication system that is aware of its surrounding environment (i.e., outside world), and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters (e.g., transmit-power, carrier-frequency, and modulation strategy) in real-time, with two primary objectives are highly reliable communications and efficient utilization of the radio [2].

In contrast to classic subspace sampling the sub Nyquist sampling samples the signal at low rate [3], [4], [5]. The theory of Compressed Sensing (CS) can lead to recovering certain signals from far fewer samples than traditional methods do if the signals are sparse in a given orthogonal basis and the sensing vectors are incoherent with the basis [6], [7], [8]. The architecture for image transmission using FPGA gives efficient reconstruction in cognitive radio is obtained in [9]. In various systems the power consumption models have been proposed and used for the power optimization by adapting modulation, coding, antenna configuration and radiated power to channel conditions and QoS requirements [10].

The rests of paper are organized as follows the image transmission model is designed in section II; in section III, System model is described IV concludes the paper and highlights merits of the research issues.

System Model

The system in fig. 1 includes cognitive radio environment, compressive spectrum sensing, and channel estimation, adjusting parameters like modulation, image compression and antenna selection to reduce the power consumption.

Fig. 1 System Architecture

The process starts with cognitive radio environment simulation which performs the spectrum sensing and channel capacity estimation using compressive sampling. According to different channel capacity estimation and modulation scheme parameters are adjusted and transmitted when the target rate is achieved with minimum number of antenna. In the receiver, the signal is demodulated, and then image is reconstructed.

Transmission and Reception

A MIMO system with 4X4 antenna is used for transmission reception. The random process x (n) is wide-sense stationary is taken for processing. The sequence observed is

(1)

The L X L auto covariance ( ) matrix for y (n) in (2) is represented with the addition of auto covariance matrix for the signal x (n) and the auto covariance matrix for the noise multiplied with the identity matrix (I)

(2)

The Singular Value Decomposition (SVD) is used for finding the eigenvalues. It is a reliable and accurate method for measuring eigen values. Let the covariance matrix be

(3)

Using the SVD, Q in (3) can be factorized as, where U, V are the left and right singular vector and ∑ = diag (λ1, λ2, λ3….. λm) is a diagonal matrix whose non negative entries are the square roots of the positive Eigen values of QQ*. * denotes the conjugate transpose. These non negative values are called singular values of Q and they are arranged in a decreasing order with the largest on in the upper left – hand corner. It is noted the QQ* = R so this method allows to calculate the Eigen values without to build the covariance matrix.

Recently, there is a need for faster sampling and less power consumption of the system. In the proposed system, this can be achieved by the compressed sampling and active branch selection of the system.

Compressed sampling specifically yields a sub-Nyquist sampling criterion. Sub-Nyquist sampling, also known as compressive sampling or compressed sensing, refers to the problem of recovering signals by samples much fewer than suggested by Nyquist rate. MWC is used to convert the analog to digital signal. The system does not require knowledge of the frequency support in either the sampling or the recovery stage. The goal of the modulator is to alias the spectrum into baseband. The modulated output is then low pass filtered and sampled at a low rate. The rate can be as low as the expected width of an individual transmission. The power spectral density (PSD) estimation of x(n) is invoked in order to locate the energy concentration within each spectrum slice. The hamming window size is W ≥ 2B/ fres. The frequency resolution is given by

(4)

The threshold (µ) is determined by

(5)

The signal detector does this giving a binary output, it is 0 when an unused frequency band is found, and it is 1 when a signal is detected. So, when an unused frequency band is detected it is allocated to the secondary user. The threshold is calculated using (5), if the power is larger than the threshold, the channel is busy and represented as 1 otherwise represented as 0. Then the image is JPEG compressed and converted to bit streams. As shown in the fig.2 the bit streams are then modulated with the OFDM modulation technique. JPEG compression process starts with 8x8 block separation then DCT is coefficients are calculated for each block. The coefficients are then quantized and run length encoded.

The time discrete equivalent is the inverse discrete Fourier transform (IDFT). So, the IDFT and the discrete Fourier transform (DFT) are used for modulating and demodulating the data constellation on the orthogonal subcarriers. The discrete samples of the OFDM symbol is given by

Where (6)

Following the parallel-to-serial (P/S) conversion, the baseband OFDM signal s(n) is upsampled and passed through the digital-to-analog (D/A) converter to convert the digital signal into an analog signal. The baseband OFDM signal is then low pass filtered, upconverted to the desired centering frequency using a mixer and a local oscillator (LO), and amplified for transmission by the power amplifier (PA). The receiver performs the reverse operation of the transmitter, mixing the RF signal to baseband for processing. Then, the signal is low pass filtered, converted to digital signal using an analog-to-digital (A/D) converter, and downsampled. The serial stream of sampled time signal is converted into parallel streams with a S/P converter and the cyclic prefix is discarded from the received composite signal, rm,n. Then, the DFT in (7) is used to transform the time domain data into frequency domain

Where (7)

These parallel streams are then demodulated to yield digital data and are multiplexed together using the P/S converter to yield the serial bit stream, and delivered to the data sink. At the receiver, frame detection is an important task. Moreover, frequency and timing synchronization is required before the OFDM symbol can be correctly demodulated. A known preamble is sent before each OFDM frame to allow receiver synchronization and channel estimation, as well as an initial acquisition of the frequency offset.

The system is provided with four antennas, these antennas are connected with power amplifiers. This PA may be class A amplifier or class B amplifier is taken into account. When the target rate is achieved then only the transmission is allowed otherwise the transmission is not allowed. The more transmit branches the MIMO system uses, the more power is consumed by the system. Therefore, the minimum number of branches (PA) as the system power consumption is linearly proportional

Fig. 2. The System Architecture

to the number of active branches as long as the target rate can be achieved.

The power of the system is calculated by considering radiated power as in (8) of PA. The number of branches is increased only when the current number of active branches cannot satisfy the rate requirement. The total radiated power (9) is calculated for the system. The power amplifiers considered are class A and class B and the power radiation for these amplifiers are calculated by (10). The radiated power from the transmit branches is

(8)

The total radiated power becomes

(9) where is the nth element of and the radiated power from transmit branch n.The system power consumption from branch can be expressed as

(10)

The system power consumption reduction is

(11)

where Pcon is the system power consumption of MIMO system with a conventional power allocation scheme (i.e., the water-filling algorithm and the equal power allocation), and Pcog the system power consumption of a MIMO system with the proposed CR framework. The MIMO systems employing Class A PAs and Class B PAs are simulated for each algorithm using (12).

Transmission Algorithm

Step 1: Signal x(t) is sensed and sampled at sub- Nyquist rate x(n).

Step 2: Channel is estimated using SVD.

Step 3: Power Spectral Density is estimated for the sampled signal x(n).

Step 4: Frequency resolution (fres), window size (W) and threshold (µ) is calculated.

Step 5: The regions which are smaller than µ are united and those regions which are smaller width than bmin are isolated.

Step 6: If there is any isolated regions

Allocate the isolated region to the user.

Source Coding is performed using JPEG Compression technique.

Channel Coding is performed using OFDM with the channel parameter calculated by Step 2.

Else go to step 1.

Step 7: Initialize the number of active antenna to one.

Step 8: Calculate and store the power allocation, target rate and achieved rate.

Step 9: Calculate the radiated power, consumed power and total consumed power.

Step 10: If power allocation achieves the target rate of the user

The signal is transmitted with that antenna configuration and achieved rate with minimum power consumption.

Else increase the number of active antenna and goto step 8.

Reception Algorithm

Step 1: Signal x(t) is sensed and sampled at sub- Nyquist rate x(n).

Step 2: Channel is estimated using SVD.

Step 3: Power Spectral Density estimated for the signal x(n).

Step 4: Frequency resolution (fres), window size (W) and threshold (µ) is calculated.

Step 5: The region of interest is isolated.

Step 6: If there is any isolated region, the channel coding is

performed using OFDM with the channel parameter

calculated by Step 2. Else go to step 1.

Step 7: JPEG decompression is done and image is reconstructed.

RESULTS AND DISCUSSION

The proposed framework is evaluated under the correlated Rayleigh fading channel and the comparison of the quasi optimal algorithm, sub optimal 1 algorithm and sub optimal 2 algorithm for power allocation scheme in terms of radiated power minimization is simulated.

For sub nyquist frequency of 10 GHz and window size is [0, 0.5] MHz, the spectrum can be divided into 195 subchannels. The threshold is calculated, if the power is larger than the threshold, the channel is busy and represented as 1 otherwise represented as 0. As shown in fig 3, fig 4, fig 5 the signal is received, sampled at the sub nyquist rate and reconstructed. The sampling duration is 0.02µs.

Fig. 3 Original signal transmitted by the receiver to estimate the MIMO channel.

Fig. 4 Original signal added with noise in the MIMO channel and sensed by the transmitter.

Fig. 5. Sub Nyquist sampled signal and the spectrum holes are detected.

The reconstructed image after transmission is shown in fig.6. The presence of blocking artifact in JPEG is unavoidable due to 8 x 8 block selector in JPEG encoder. The PSNR value obtained after reconstruction of the image is 33.96. The bit stream of the image is modulated with the OFDM modulation and the bandpass signal is shown in fig. 7. The orthogonality helps to transmits more data simultaneously. Comparing the power savings between the quasi optimal, sub optimal 1 and sub optimal 2 in Figs. 8 and 9 it is clear that at some target rates, using more antennas can achieve further power saving.

Fig.6. Input image and reconstructed image at the receiver.

Fig.7. Bandpass signal of the Orthogonal Frequency Division Multiplexing (OFDM) in cognitive radio systems.

Fig. 8. Power saving of a MIMO systems for class A PA.

Fig. 9. Power saving of a MIMO systems for class B PA.

CONCLUSION

Thus the image is transmitted over a MIMO using cognitive radio approach with minimal power consumption of the system. The sub- Nyquist sampling system is used for the ADC & DAC of the OFDM which ensures the faster with low rate processing and this gives the ability to process the transmitted bands without requiring the prior knowledge of it. This helps the CR to understand the channel, transmitter, receiver, bandwidth response etc. The systems power consumption is reduced up to 80% than the conventional power allocation method. This framework and algorithms achieve significant power reduction for power Amplifier.